





'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np






def   __A    (				a_    :	str   ,a_    :	Union[str, Any]   ,a_    :	int						):
		lowerCAmelCase  :     Optional[int]       						= 0
		if start < end:
				lowerCAmelCase  :     Dict       						= randint(a_   ,a_						)
				lowerCAmelCase  :     List[str]       						= a[end]
				lowerCAmelCase  :     Any       						= a[pivot]
				lowerCAmelCase  :     Tuple       						= temp

				lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= _in_place_partition(a_   ,a_   ,a_						)
				count += _in_place_quick_sort(a_   ,a_   ,p - 1						)
				count += _in_place_quick_sort(a_   ,p + 1   ,a_						)
		return count






def   __A    (				a_    :	Dict   ,a_    :	Tuple   ,a_    :	int						):
		lowerCAmelCase  :     str       						= 0
		lowerCAmelCase  :     Dict       						= randint(a_   ,a_						)
		lowerCAmelCase  :     Optional[int]       						= a[end]
		lowerCAmelCase  :     Optional[int]       						= a[pivot]
		lowerCAmelCase  :     int       						= temp
		lowerCAmelCase  :     Optional[Any]       						= start - 1
		for index in range(a_   ,a_						):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						lowerCAmelCase  :     int       						= new_pivot_index + 1
						lowerCAmelCase  :     Optional[Any]       						= a[new_pivot_index]
						lowerCAmelCase  :     Optional[Any]       						= a[index]
						lowerCAmelCase  :     Tuple       						= temp

		lowerCAmelCase  :     Optional[int]       						= a[new_pivot_index + 1]
		lowerCAmelCase  :     int       						= a[end]
		lowerCAmelCase  :     Dict       						= temp
		return new_pivot_index + 1, count


lowerCAmelCase									=   TemporaryFile()
lowerCAmelCase									=   1_00  # 1000 elements are to be sorted


lowerCAmelCase		,    lowerCAmelCase									=   0, 1  # mean and standard deviation
lowerCAmelCase									=   np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
lowerCAmelCase									=   np.load(outfile)
lowerCAmelCase									=   len(M) - 1
lowerCAmelCase									=   _in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)

print(z)










'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



